---
title: "AzureOpenAIDocumentEmbedder"
id: azureopenaidocumentembedder
slug: "/azureopenaidocumentembedder"
description: "This component computes the embeddings of a list of documents and stores the obtained vectors in the embedding field of each document. It uses Azure cognitive services for text and document embedding with models deployed on Azure."
---

# AzureOpenAIDocumentEmbedder

This component computes the embeddings of a list of documents and stores the obtained vectors in the embedding field of each document. It uses Azure cognitive services for text and document embedding with models deployed on Azure.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Before a [`DocumentWriter`](../writers/documentwriter.mdx) |
| **Mandatory init variables** | `api_key`: The Azure OpenAI API key. Can be set with `AZURE_OPENAI_API_KEY` env var.  <br />`azure_endpoint`: The endpoint of the model deployed on Azure. |
| **Mandatory run variables** | `documents`: A list of documents |
| **Output variables** | `documents`: A list of documents (enriched with embeddings)  <br /> <br />`meta`: A dictionary of metadata |
| **API reference** | [Embedders](/reference/embedders-api) |
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/azure_document_embedder.py |

</div>

## Overview

The vectors computed by this component are necessary to perform embedding retrieval on a collection of documents. At retrieval time, the vector representing the query is compared with those of the documents to find the most similar or relevant documents.

To see the list of compatible embedding models, head over to Azure [documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models?source=recommendations). The default model for `AzureOpenAITextEmbedder` is `text-embedding-ada-002`.

This component should be used to embed a list of documents. To embed a string, you should use the [`AzureOpenAITextEmbedder`](azureopenaitextembedder.mdx).

To work with Azure components, you will need an Azure OpenAI API key, as well as an Azure OpenAI Endpoint. You can learn more about them in Azure [documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference).

The component uses `AZURE_OPENAI_API_KEY` or `AZURE_OPENAI_AD_TOKEN` environment variables by default. Otherwise, you can pass `api_key` or `azure_ad_token` at initialization:

```python
client = AzureOpenAIDocumentEmbedder(azure_endpoint="<Your Azure endpoint e.g. `https://your-company.azure.openai.com/>",
                        api_key=Secret.from_token("<your-api-key>"),
                        azure_deployment="<a model name>")
```

:::info
We recommend using environment variables instead of initialization parameters.
:::

### Embedding Metadata

Text documents often come with a set of metadata. If they are distinctive and semantically meaningful, you can embed them along with the text of the document to improve retrieval.

You can do this easily by using the Document Embedder:

```python
from haystack import Document
from haystack.components.embedders import AzureOpenAIDocumentEmbedder

doc = Document(content="some text",meta={"title": "relevant title", "page number": 18})

embedder = AzureOpenAIDocumentEmbedder(meta_fields_to_embed=["title"])

docs_w_embeddings = embedder.run(documents=[doc])["documents"]

```

## Usage

### On its own

```python
from haystack import Document
from haystack.components.embedders import AzureOpenAIDocumentEmbedder

doc = Document(content="I love pizza!")

document_embedder = AzureOpenAIDocumentEmbedder()

result = document_embedder.run([doc])
print(result['documents'][0].embedding)

## [0.017020374536514282, -0.023255806416273117, ...]
```

### In a pipeline

```python
from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.embedders import AzureOpenAITextEmbedder, AzureOpenAIDocumentEmbedder
from haystack.components.writers import DocumentWriter
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever

document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")

documents = [Document(content="My name is Wolfgang and I live in Berlin"),
             Document(content="I saw a black horse running"),
             Document(content="Germany has many big cities")]

indexing_pipeline = Pipeline()
indexing_pipeline.add_component("embedder", AzureOpenAIDocumentEmbedder())
indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))
indexing_pipeline.connect("embedder", "writer")

indexing_pipeline.run({"embedder": {"documents": documents}})

query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", AzureOpenAITextEmbedder())
query_pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

query = "Who lives in Berlin?"

result = query_pipeline.run({"text_embedder":{"text": query}})

print(result['retriever']['documents'][0])

## Document(id=..., mimetype: 'text/plain',
## text: 'My name is Wolfgang and I live in Berlin')
```
